Overview

Brought to you by YData

Dataset statistics

Number of variables35
Number of observations473
Missing cells2723
Missing cells (%)16.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory129.5 KiB
Average record size in memory280.3 B

Variable types

Numeric9
Text8
Categorical17
Unsupported1

Alerts

Elogio has constant value "Não" Constant
Crítica has constant value "Não" Constant
Repescagem has constant value "Não" Constant
Bonificado is highly overall correlated with Motivo da BonificaçãoHigh correlation
Canal is highly overall correlated with Plano and 2 other fieldsHigh correlation
Cidade is highly overall correlated with Contratante and 3 other fieldsHigh correlation
Contratante is highly overall correlated with Cidade and 6 other fieldsHigh correlation
Criado is highly overall correlated with ID and 2 other fieldsHigh correlation
DDD is highly overall correlated with Cidade and 2 other fieldsHigh correlation
Estado is highly overall correlated with Cidade and 3 other fieldsHigh correlation
ID is highly overall correlated with Criado and 2 other fieldsHigh correlation
ID Anunciante is highly overall correlated with Contratante and 2 other fieldsHigh correlation
ID Campanha is highly overall correlated with Contratante and 1 other fieldsHigh correlation
ID Cliente is highly overall correlated with Motivo da Bonificação and 1 other fieldsHigh correlation
ID Contratante is highly overall correlated with Cidade and 6 other fieldsHigh correlation
Motivo da Bonificação is highly overall correlated with Bonificado and 1 other fieldsHigh correlation
Plano is highly overall correlated with Canal and 3 other fieldsHigh correlation
Tipo is highly overall correlated with UTM Campaign and 2 other fieldsHigh correlation
UTM Campaign is highly overall correlated with Canal and 5 other fieldsHigh correlation
UTM Content is highly overall correlated with Canal and 5 other fieldsHigh correlation
UTM Medium is highly overall correlated with Tipo and 3 other fieldsHigh correlation
UTM Source is highly overall correlated with UTM Campaign and 2 other fieldsHigh correlation
Unnamed: 34 is highly overall correlated with Contratante and 4 other fieldsHigh correlation
Últ. Atualização is highly overall correlated with Criado and 2 other fieldsHigh correlation
Cidade is highly imbalanced (59.6%) Imbalance
Estado is highly imbalanced (74.6%) Imbalance
Motivo da Bonificação is highly imbalanced (62.5%) Imbalance
Unnamed: 34 is highly imbalanced (71.0%) Imbalance
Plano has 42 (8.9%) missing values Missing
ID Contratante has 463 (97.9%) missing values Missing
Contratante has 463 (97.9%) missing values Missing
ID Campanha has 42 (8.9%) missing values Missing
Campanha has 42 (8.9%) missing values Missing
Mensagem has 73 (15.4%) missing values Missing
Recibo has 304 (64.3%) missing values Missing
Motivo da Bonificação has 408 (86.3%) missing values Missing
Feedback has 473 (100.0%) missing values Missing
UTM Campaign has 193 (40.8%) missing values Missing
UTM Content has 220 (46.5%) missing values Missing
ID is uniformly distributed Uniform
ID has unique values Unique
Feedback is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-03-19 13:02:56.459902
Analysis finished2025-03-19 13:03:06.378191
Duration9.92 seconds
Software versionydata-profiling vv4.15.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct473
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean829005
Minimum828769
Maximum829241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:06.541999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum828769
5-th percentile828792.6
Q1828887
median829005
Q3829123
95-th percentile829217.4
Maximum829241
Range472
Interquartile range (IQR)236

Descriptive statistics

Standard deviation136.6876
Coefficient of variation (CV)0.00016488151
Kurtosis-1.2
Mean829005
Median Absolute Deviation (MAD)118
Skewness0
Sum3.9211936 × 108
Variance18683.5
MonotonicityStrictly increasing
2025-03-19T10:03:06.834100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
829241 1
 
0.2%
828769 1
 
0.2%
828770 1
 
0.2%
828771 1
 
0.2%
828772 1
 
0.2%
828773 1
 
0.2%
828774 1
 
0.2%
828775 1
 
0.2%
828776 1
 
0.2%
829225 1
 
0.2%
Other values (463) 463
97.9%
ValueCountFrequency (%)
828769 1
0.2%
828770 1
0.2%
828771 1
0.2%
828772 1
0.2%
828773 1
0.2%
828774 1
0.2%
828775 1
0.2%
828776 1
0.2%
828777 1
0.2%
828778 1
0.2%
ValueCountFrequency (%)
829241 1
0.2%
829240 1
0.2%
829239 1
0.2%
829238 1
0.2%
829237 1
0.2%
829236 1
0.2%
829235 1
0.2%
829234 1
0.2%
829233 1
0.2%
829232 1
0.2%

ID Cliente
Real number (ℝ)

High correlation 

Distinct263
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean461769.34
Minimum1888
Maximum478153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:07.001912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1888
5-th percentile436568
Q1477869
median478002
Q3478055
95-th percentile478131
Maximum478153
Range476265
Interquartile range (IQR)186

Descriptive statistics

Standard deviation68162.041
Coefficient of variation (CV)0.14761058
Kurtosis29.930371
Mean461769.34
Median Absolute Deviation (MAD)74
Skewness-5.4116632
Sum2.184169 × 108
Variance4.6460639 × 109
MonotonicityNot monotonic
2025-03-19T10:03:07.167925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
478020 59
 
12.5%
445525 22
 
4.7%
477928 10
 
2.1%
477711 6
 
1.3%
477938 6
 
1.3%
436568 5
 
1.1%
478035 5
 
1.1%
477880 4
 
0.8%
476919 4
 
0.8%
462038 4
 
0.8%
Other values (253) 348
73.6%
ValueCountFrequency (%)
1888 2
0.4%
5368 1
0.2%
15817 1
0.2%
16908 1
0.2%
46718 1
0.2%
55900 2
0.4%
158796 1
0.2%
164947 2
0.4%
183741 1
0.2%
255321 1
0.2%
ValueCountFrequency (%)
478153 1
 
0.2%
478152 1
 
0.2%
478150 2
0.4%
478149 1
 
0.2%
478148 1
 
0.2%
478147 1
 
0.2%
478146 3
0.6%
478145 2
0.4%
478143 1
 
0.2%
478142 1
 
0.2%
Distinct253
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:07.405802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length52
Median length32
Mean length11.89852
Min length2

Characters and Unicode

Total characters5628
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)35.5%

Sample

1st rowJuliana Barbour
2nd rowMárcia
3rd rowAline Rodrigues
4th rowPaloma Herbst
5th rowLuiz Fernando
ValueCountFrequency (%)
ângela 60
 
7.0%
fernanda 32
 
3.7%
de 26
 
3.0%
santos 14
 
1.6%
ribeiro 12
 
1.4%
bruno 11
 
1.3%
eliana 10
 
1.2%
karina 10
 
1.2%
brabo 10
 
1.2%
oliveira 9
 
1.0%
Other values (368) 665
77.4%
2025-03-19T10:03:07.739736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 792
14.1%
e 524
 
9.3%
i 448
 
8.0%
n 431
 
7.7%
386
 
6.9%
r 385
 
6.8%
l 335
 
6.0%
o 325
 
5.8%
s 207
 
3.7%
d 144
 
2.6%
Other values (56) 1651
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 792
14.1%
e 524
 
9.3%
i 448
 
8.0%
n 431
 
7.7%
386
 
6.9%
r 385
 
6.8%
l 335
 
6.0%
o 325
 
5.8%
s 207
 
3.7%
d 144
 
2.6%
Other values (56) 1651
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 792
14.1%
e 524
 
9.3%
i 448
 
8.0%
n 431
 
7.7%
386
 
6.9%
r 385
 
6.8%
l 335
 
6.0%
o 325
 
5.8%
s 207
 
3.7%
d 144
 
2.6%
Other values (56) 1651
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 792
14.1%
e 524
 
9.3%
i 448
 
8.0%
n 431
 
7.7%
386
 
6.9%
r 385
 
6.8%
l 335
 
6.0%
o 325
 
5.8%
s 207
 
3.7%
d 144
 
2.6%
Other values (56) 1651
29.3%

E-mail
Text

Distinct263
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:07.890767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length31
Mean length23.805497
Min length11

Characters and Unicode

Total characters11260
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)37.4%

Sample

1st rowjuliana@skillsonline.com.br
2nd rowmarcinha_mg27@hotmail.com
3rd rowali.rodrigues@outlook.com.br
4th rowpallomaherbst@gmail.com
5th rowluiz.santos2981@gmail.com
ValueCountFrequency (%)
apaleari@cusa.canon.com 59
 
12.5%
febasaglia@hotmail.com 22
 
4.7%
braboeliana2@gmail.com 10
 
2.1%
alethearsoliveira@gail.com 6
 
1.3%
karinabuenotolardo@gmail.com 6
 
1.3%
dheborafernanda090@gmail.com 5
 
1.1%
pkkarol375@gmail.com 5
 
1.1%
karina.dafonsecaborges@gmail.com 4
 
0.8%
leon.lacerda2015@gmail.com 4
 
0.8%
dyandraevelyn@gmail.com 4
 
0.8%
Other values (253) 348
73.6%
2025-03-19T10:03:08.182564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1471
13.1%
o 1042
 
9.3%
m 960
 
8.5%
i 869
 
7.7%
l 785
 
7.0%
c 746
 
6.6%
. 647
 
5.7%
e 541
 
4.8%
r 503
 
4.5%
@ 473
 
4.2%
Other values (30) 3223
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1471
13.1%
o 1042
 
9.3%
m 960
 
8.5%
i 869
 
7.7%
l 785
 
7.0%
c 746
 
6.6%
. 647
 
5.7%
e 541
 
4.8%
r 503
 
4.5%
@ 473
 
4.2%
Other values (30) 3223
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1471
13.1%
o 1042
 
9.3%
m 960
 
8.5%
i 869
 
7.7%
l 785
 
7.0%
c 746
 
6.6%
. 647
 
5.7%
e 541
 
4.8%
r 503
 
4.5%
@ 473
 
4.2%
Other values (30) 3223
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1471
13.1%
o 1042
 
9.3%
m 960
 
8.5%
i 869
 
7.7%
l 785
 
7.0%
c 746
 
6.6%
. 647
 
5.7%
e 541
 
4.8%
r 503
 
4.5%
@ 473
 
4.2%
Other values (30) 3223
28.6%

DDD
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.054968
Minimum11
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:08.270533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median11
Q312
95-th percentile71
Maximum98
Range87
Interquartile range (IQR)1

Descriptive statistics

Standard deviation20.794218
Coefficient of variation (CV)1.0368612
Kurtosis4.2002592
Mean20.054968
Median Absolute Deviation (MAD)0
Skewness2.338966
Sum9486
Variance432.39951
MonotonicityNot monotonic
2025-03-19T10:03:08.372973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
11 352
74.4%
21 16
 
3.4%
61 13
 
2.7%
71 11
 
2.3%
19 9
 
1.9%
92 9
 
1.9%
31 8
 
1.7%
15 6
 
1.3%
12 5
 
1.1%
62 5
 
1.1%
Other values (19) 39
 
8.2%
ValueCountFrequency (%)
11 352
74.4%
12 5
 
1.1%
13 4
 
0.8%
14 1
 
0.2%
15 6
 
1.3%
16 1
 
0.2%
18 3
 
0.6%
19 9
 
1.9%
21 16
 
3.4%
27 4
 
0.8%
ValueCountFrequency (%)
98 2
 
0.4%
92 9
1.9%
89 1
 
0.2%
85 4
 
0.8%
83 1
 
0.2%
82 2
 
0.4%
81 1
 
0.2%
75 1
 
0.2%
71 11
2.3%
67 3
 
0.6%

Telefone
Real number (ℝ)

Distinct262
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7402631 × 108
Minimum9.1037843 × 108
Maximum1 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:08.491928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.1037843 × 108
5-th percentile9.4319536 × 108
Q19.5977858 × 108
median9.7655486 × 108
Q39.8934883 × 108
95-th percentile9.9933871 × 108
Maximum1 × 109
Range89621573
Interquartile range (IQR)29570257

Descriptive statistics

Standard deviation18731917
Coefficient of variation (CV)0.019231428
Kurtosis0.13578572
Mean9.7402631 × 108
Median Absolute Deviation (MAD)16324972
Skewness-0.67239795
Sum4.6071445 × 1011
Variance3.508847 × 1014
MonotonicityNot monotonic
2025-03-19T10:03:08.620637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
959778575 59
 
12.5%
971890699 22
 
4.7%
958382589 10
 
2.1%
986351223 6
 
1.3%
943195362 6
 
1.3%
970666784 5
 
1.1%
984668015 5
 
1.1%
982441170 4
 
0.8%
951339972 4
 
0.8%
953248804 4
 
0.8%
Other values (252) 348
73.6%
ValueCountFrequency (%)
910378426 1
 
0.2%
913413403 2
0.4%
913561272 1
 
0.2%
916935898 2
0.4%
918778982 2
0.4%
930032732 1
 
0.2%
930122220 1
 
0.2%
930323570 3
0.6%
930653452 1
 
0.2%
940081036 2
0.4%
ValueCountFrequency (%)
999999999 1
 
0.2%
999994444 2
0.4%
999937332 3
0.6%
999928080 2
0.4%
999905468 1
 
0.2%
999898979 2
0.4%
999843963 1
 
0.2%
999816442 3
0.6%
999804113 2
0.4%
999749199 1
 
0.2%

ID Imóvel
Real number (ℝ)

Distinct333
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5751.1121
Minimum225
Maximum8286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:08.790134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum225
5-th percentile2072
Q13899
median6510
Q37414
95-th percentile8058.8
Maximum8286
Range8061
Interquartile range (IQR)3515

Descriptive statistics

Standard deviation2028.3755
Coefficient of variation (CV)0.35269274
Kurtosis-0.77326941
Mean5751.1121
Median Absolute Deviation (MAD)1322
Skewness-0.6740833
Sum2720276
Variance4114307.1
MonotonicityNot monotonic
2025-03-19T10:03:09.006664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7918 6
 
1.3%
7987 5
 
1.1%
6582 5
 
1.1%
4721 5
 
1.1%
5442 5
 
1.1%
7560 5
 
1.1%
5015 4
 
0.8%
7415 4
 
0.8%
6998 4
 
0.8%
3850 4
 
0.8%
Other values (323) 426
90.1%
ValueCountFrequency (%)
225 1
 
0.2%
896 2
0.4%
1049 3
0.6%
1176 1
 
0.2%
1295 1
 
0.2%
1315 1
 
0.2%
1331 1
 
0.2%
1335 1
 
0.2%
1365 1
 
0.2%
1431 1
 
0.2%
ValueCountFrequency (%)
8286 2
0.4%
8279 1
0.2%
8254 2
0.4%
8245 1
0.2%
8241 1
0.2%
8234 1
0.2%
8231 1
0.2%
8227 1
0.2%
8218 1
0.2%
8215 1
0.2%
Distinct333
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:09.337868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length30
Mean length17.919662
Min length3

Characters and Unicode

Total characters8476
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique249 ?
Unique (%)52.6%

Sample

1st rowReserva das Árvores - Paineiras
2nd rowResidencial Pedra de Guaratiba
3rd rowNostro Mooca
4th rowReserva Direcional Limão
5th rowResidencial Turim
ValueCountFrequency (%)
vila 41
 
3.0%
by 35
 
2.5%
20
 
1.4%
living 19
 
1.4%
da 17
 
1.2%
sensia 15
 
1.1%
ipiranga 14
 
1.0%
vivaz 13
 
0.9%
mooca 13
 
0.9%
clube 13
 
0.9%
Other values (505) 1188
85.6%
2025-03-19T10:03:09.821138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 999
 
11.8%
914
 
10.8%
i 657
 
7.8%
o 585
 
6.9%
e 553
 
6.5%
r 446
 
5.3%
n 412
 
4.9%
l 305
 
3.6%
s 301
 
3.6%
t 276
 
3.3%
Other values (77) 3028
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 999
 
11.8%
914
 
10.8%
i 657
 
7.8%
o 585
 
6.9%
e 553
 
6.5%
r 446
 
5.3%
n 412
 
4.9%
l 305
 
3.6%
s 301
 
3.6%
t 276
 
3.3%
Other values (77) 3028
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 999
 
11.8%
914
 
10.8%
i 657
 
7.8%
o 585
 
6.9%
e 553
 
6.5%
r 446
 
5.3%
n 412
 
4.9%
l 305
 
3.6%
s 301
 
3.6%
t 276
 
3.3%
Other values (77) 3028
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 999
 
11.8%
914
 
10.8%
i 657
 
7.8%
o 585
 
6.9%
e 553
 
6.5%
r 446
 
5.3%
n 412
 
4.9%
l 305
 
3.6%
s 301
 
3.6%
t 276
 
3.3%
Other values (77) 3028
35.7%

Plano
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.5%
Missing42
Missing (%)8.9%
Memory size3.8 KiB
Business
269 
Brokers
162 

Length

Max length8
Median length8
Mean length7.6241299
Min length7

Characters and Unicode

Total characters3286
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrokers
2nd rowBusiness
3rd rowBusiness
4th rowBusiness
5th rowBusiness

Common Values

ValueCountFrequency (%)
Business 269
56.9%
Brokers 162
34.2%
(Missing) 42
 
8.9%

Length

2025-03-19T10:03:09.927285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:10.020430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
business 269
62.4%
brokers 162
37.6%

Most occurring characters

ValueCountFrequency (%)
s 969
29.5%
B 431
13.1%
e 431
13.1%
r 324
 
9.9%
u 269
 
8.2%
n 269
 
8.2%
i 269
 
8.2%
o 162
 
4.9%
k 162
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3286
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 969
29.5%
B 431
13.1%
e 431
13.1%
r 324
 
9.9%
u 269
 
8.2%
n 269
 
8.2%
i 269
 
8.2%
o 162
 
4.9%
k 162
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3286
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 969
29.5%
B 431
13.1%
e 431
13.1%
r 324
 
9.9%
u 269
 
8.2%
n 269
 
8.2%
i 269
 
8.2%
o 162
 
4.9%
k 162
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3286
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 969
29.5%
B 431
13.1%
e 431
13.1%
r 324
 
9.9%
u 269
 
8.2%
n 269
 
8.2%
i 269
 
8.2%
o 162
 
4.9%
k 162
 
4.9%

Cidade
Categorical

High correlation  Imbalance 

Distinct38
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
São Paulo
344 
Rio de Janeiro
 
14
Salvador
 
12
Osasco
 
12
Manaus
 
10
Other values (33)
81 

Length

Max length21
Median length9
Mean length9.05074
Min length5

Characters and Unicode

Total characters4281
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)2.5%

Sample

1st rowCotia
2nd rowRio de Janeiro
3rd rowSão Paulo
4th rowSão Paulo
5th rowCuritiba

Common Values

ValueCountFrequency (%)
São Paulo 344
72.7%
Rio de Janeiro 14
 
3.0%
Salvador 12
 
2.5%
Osasco 12
 
2.5%
Manaus 10
 
2.1%
Guarulhos 9
 
1.9%
Santo André 7
 
1.5%
Fortaleza 4
 
0.8%
Carapicuíba 4
 
0.8%
Goiânia 4
 
0.8%
Other values (28) 53
 
11.2%

Length

2025-03-19T10:03:10.139776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
são 350
39.9%
paulo 344
39.2%
rio 14
 
1.6%
de 14
 
1.6%
janeiro 14
 
1.6%
salvador 12
 
1.4%
osasco 12
 
1.4%
manaus 10
 
1.1%
guarulhos 9
 
1.0%
santo 7
 
0.8%
Other values (43) 91
 
10.4%

Most occurring characters

ValueCountFrequency (%)
o 823
19.2%
a 512
12.0%
404
9.4%
u 391
9.1%
l 383
8.9%
S 377
8.8%
ã 352
8.2%
P 349
8.2%
r 80
 
1.9%
i 75
 
1.8%
Other values (35) 535
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 823
19.2%
a 512
12.0%
404
9.4%
u 391
9.1%
l 383
8.9%
S 377
8.8%
ã 352
8.2%
P 349
8.2%
r 80
 
1.9%
i 75
 
1.8%
Other values (35) 535
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 823
19.2%
a 512
12.0%
404
9.4%
u 391
9.1%
l 383
8.9%
S 377
8.8%
ã 352
8.2%
P 349
8.2%
r 80
 
1.9%
i 75
 
1.8%
Other values (35) 535
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 823
19.2%
a 512
12.0%
404
9.4%
u 391
9.1%
l 383
8.9%
S 377
8.8%
ã 352
8.2%
P 349
8.2%
r 80
 
1.9%
i 75
 
1.8%
Other values (35) 535
12.5%

Estado
Categorical

High correlation  Imbalance 

Distinct15
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
São Paulo
410 
Rio de Janeiro
 
16
Bahia
 
12
Amazonas
 
10
Ceará
 
4
Other values (10)
 
21

Length

Max length17
Median length9
Mean length9.0909091
Min length5

Characters and Unicode

Total characters4300
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st rowSão Paulo
2nd rowRio de Janeiro
3rd rowSão Paulo
4th rowSão Paulo
5th rowParaná

Common Values

ValueCountFrequency (%)
São Paulo 410
86.7%
Rio de Janeiro 16
 
3.4%
Bahia 12
 
2.5%
Amazonas 10
 
2.1%
Ceará 4
 
0.8%
Goiás 4
 
0.8%
Rio Grande do Sul 3
 
0.6%
Distrito Federal 3
 
0.6%
Minas Gerais 3
 
0.6%
Alagoas 2
 
0.4%
Other values (5) 6
 
1.3%

Length

2025-03-19T10:03:10.410345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
são 410
44.0%
paulo 410
44.0%
rio 19
 
2.0%
de 16
 
1.7%
janeiro 16
 
1.7%
bahia 12
 
1.3%
amazonas 10
 
1.1%
ceará 4
 
0.4%
goiás 4
 
0.4%
grande 3
 
0.3%
Other values (14) 28
 
3.0%

Most occurring characters

ValueCountFrequency (%)
o 880
20.5%
a 504
11.7%
459
10.7%
l 418
9.7%
S 415
9.7%
P 414
9.6%
u 414
9.6%
ã 410
9.5%
i 65
 
1.5%
e 49
 
1.1%
Other values (24) 272
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 880
20.5%
a 504
11.7%
459
10.7%
l 418
9.7%
S 415
9.7%
P 414
9.6%
u 414
9.6%
ã 410
9.5%
i 65
 
1.5%
e 49
 
1.1%
Other values (24) 272
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 880
20.5%
a 504
11.7%
459
10.7%
l 418
9.7%
S 415
9.7%
P 414
9.6%
u 414
9.6%
ã 410
9.5%
i 65
 
1.5%
e 49
 
1.1%
Other values (24) 272
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 880
20.5%
a 504
11.7%
459
10.7%
l 418
9.7%
S 415
9.7%
P 414
9.6%
u 414
9.6%
ã 410
9.5%
i 65
 
1.5%
e 49
 
1.1%
Other values (24) 272
 
6.3%

ID Anunciante
Real number (ℝ)

High correlation 

Distinct139
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1161.0254
Minimum47
Maximum2551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:10.553739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile111
Q1355
median1007
Q31991
95-th percentile2208
Maximum2551
Range2504
Interquartile range (IQR)1636

Descriptive statistics

Standard deviation794.06218
Coefficient of variation (CV)0.68393181
Kurtosis-1.5807245
Mean1161.0254
Median Absolute Deviation (MAD)776
Skewness0.080870425
Sum549165
Variance630534.75
MonotonicityNot monotonic
2025-03-19T10:03:10.763613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
714 24
 
5.1%
157 16
 
3.4%
1472 15
 
3.2%
1991 15
 
3.2%
292 14
 
3.0%
111 13
 
2.7%
2208 12
 
2.5%
498 10
 
2.1%
105 10
 
2.1%
231 9
 
1.9%
Other values (129) 335
70.8%
ValueCountFrequency (%)
47 3
 
0.6%
103 1
 
0.2%
105 10
2.1%
111 13
2.7%
133 8
1.7%
146 5
 
1.1%
155 7
1.5%
157 16
3.4%
160 2
 
0.4%
180 3
 
0.6%
ValueCountFrequency (%)
2551 1
 
0.2%
2512 2
0.4%
2511 2
0.4%
2500 4
0.8%
2382 1
 
0.2%
2336 1
 
0.2%
2334 2
0.4%
2289 1
 
0.2%
2237 1
 
0.2%
2229 1
 
0.2%
Distinct142
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:11.378792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length29
Mean length15.245243
Min length3

Characters and Unicode

Total characters7211
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)12.1%

Sample

1st rowImo House Negócios Imobiliários
2nd rowMRV
3rd rowEmccamp
4th rowDirecional Engenharia
5th rowTNR Construtora
ValueCountFrequency (%)
de 30
 
2.8%
engenharia 28
 
2.7%
incorporadora 26
 
2.5%
direcional 24
 
2.3%
da 23
 
2.2%
barbosa 20
 
1.9%
silva 20
 
1.9%
living 16
 
1.5%
tegra 15
 
1.4%
residencial 15
 
1.4%
Other values (253) 839
79.5%
2025-03-19T10:03:12.222643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 797
 
11.1%
e 584
 
8.1%
583
 
8.1%
o 561
 
7.8%
i 529
 
7.3%
r 526
 
7.3%
n 440
 
6.1%
s 342
 
4.7%
d 264
 
3.7%
l 242
 
3.4%
Other values (52) 2343
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7211
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 797
 
11.1%
e 584
 
8.1%
583
 
8.1%
o 561
 
7.8%
i 529
 
7.3%
r 526
 
7.3%
n 440
 
6.1%
s 342
 
4.7%
d 264
 
3.7%
l 242
 
3.4%
Other values (52) 2343
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7211
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 797
 
11.1%
e 584
 
8.1%
583
 
8.1%
o 561
 
7.8%
i 529
 
7.3%
r 526
 
7.3%
n 440
 
6.1%
s 342
 
4.7%
d 264
 
3.7%
l 242
 
3.4%
Other values (52) 2343
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7211
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 797
 
11.1%
e 584
 
8.1%
583
 
8.1%
o 561
 
7.8%
i 529
 
7.3%
r 526
 
7.3%
n 440
 
6.1%
s 342
 
4.7%
d 264
 
3.7%
l 242
 
3.4%
Other values (52) 2343
32.5%

ID Contratante
Categorical

High correlation  Missing 

Distinct4
Distinct (%)40.0%
Missing463
Missing (%)97.9%
Memory size3.8 KiB
5.0
144.0
159.0
1.0

Length

Max length5
Median length3
Mean length3.4
Min length3

Characters and Unicode

Total characters34
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)30.0%

Sample

1st row5.0
2nd row5.0
3rd row144.0
4th row5.0
5th row159.0

Common Values

ValueCountFrequency (%)
5.0 7
 
1.5%
144.0 1
 
0.2%
159.0 1
 
0.2%
1.0 1
 
0.2%
(Missing) 463
97.9%

Length

2025-03-19T10:03:12.396633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:12.538080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5.0 7
70.0%
144.0 1
 
10.0%
159.0 1
 
10.0%
1.0 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
. 10
29.4%
0 10
29.4%
5 8
23.5%
1 3
 
8.8%
4 2
 
5.9%
9 1
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 10
29.4%
0 10
29.4%
5 8
23.5%
1 3
 
8.8%
4 2
 
5.9%
9 1
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 10
29.4%
0 10
29.4%
5 8
23.5%
1 3
 
8.8%
4 2
 
5.9%
9 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 10
29.4%
0 10
29.4%
5 8
23.5%
1 3
 
8.8%
4 2
 
5.9%
9 1
 
2.9%

Contratante
Categorical

High correlation  Missing 

Distinct4
Distinct (%)40.0%
Missing463
Missing (%)97.9%
Memory size3.8 KiB
Tegra SP
REM
Grupo Luni - Cumbuca
Cyrela SP

Length

Max length20
Median length8
Mean length8.8
Min length3

Characters and Unicode

Total characters88
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)30.0%

Sample

1st rowTegra SP
2nd rowTegra SP
3rd rowREM
4th rowTegra SP
5th rowGrupo Luni - Cumbuca

Common Values

ValueCountFrequency (%)
Tegra SP 7
 
1.5%
REM 1
 
0.2%
Grupo Luni - Cumbuca 1
 
0.2%
Cyrela SP 1
 
0.2%
(Missing) 463
97.9%

Length

2025-03-19T10:03:12.683810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:12.819806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sp 8
38.1%
tegra 7
33.3%
rem 1
 
4.8%
grupo 1
 
4.8%
luni 1
 
4.8%
1
 
4.8%
cumbuca 1
 
4.8%
cyrela 1
 
4.8%

Most occurring characters

ValueCountFrequency (%)
11
12.5%
r 9
10.2%
a 9
10.2%
P 8
9.1%
e 8
9.1%
S 8
9.1%
T 7
8.0%
g 7
8.0%
u 4
 
4.5%
C 2
 
2.3%
Other values (15) 15
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 88
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11
12.5%
r 9
10.2%
a 9
10.2%
P 8
9.1%
e 8
9.1%
S 8
9.1%
T 7
8.0%
g 7
8.0%
u 4
 
4.5%
C 2
 
2.3%
Other values (15) 15
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 88
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11
12.5%
r 9
10.2%
a 9
10.2%
P 8
9.1%
e 8
9.1%
S 8
9.1%
T 7
8.0%
g 7
8.0%
u 4
 
4.5%
C 2
 
2.3%
Other values (15) 15
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 88
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11
12.5%
r 9
10.2%
a 9
10.2%
P 8
9.1%
e 8
9.1%
S 8
9.1%
T 7
8.0%
g 7
8.0%
u 4
 
4.5%
C 2
 
2.3%
Other values (15) 15
17.0%

ID Campanha
Real number (ℝ)

High correlation  Missing 

Distinct178
Distinct (%)41.3%
Missing42
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean1628.9374
Minimum3
Maximum2201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:13.036807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile895
Q11371
median1723
Q31943.5
95-th percentile2138
Maximum2201
Range2198
Interquartile range (IQR)572.5

Descriptive statistics

Standard deviation439.84763
Coefficient of variation (CV)0.2700212
Kurtosis2.9898952
Mean1628.9374
Median Absolute Deviation (MAD)300
Skewness-1.4168486
Sum702072
Variance193465.93
MonotonicityNot monotonic
2025-03-19T10:03:13.287904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1943 15
 
3.2%
1828 10
 
2.1%
3 10
 
2.1%
1670 8
 
1.7%
1161 8
 
1.7%
1493 8
 
1.7%
1731 8
 
1.7%
1343 7
 
1.5%
1370 6
 
1.3%
1893 6
 
1.3%
Other values (168) 345
72.9%
(Missing) 42
 
8.9%
ValueCountFrequency (%)
3 10
2.1%
113 1
 
0.2%
117 1
 
0.2%
451 1
 
0.2%
736 3
 
0.6%
753 1
 
0.2%
759 1
 
0.2%
861 2
 
0.4%
887 1
 
0.2%
895 3
 
0.6%
ValueCountFrequency (%)
2201 1
 
0.2%
2194 1
 
0.2%
2192 2
0.4%
2186 1
 
0.2%
2184 1
 
0.2%
2181 2
0.4%
2174 1
 
0.2%
2172 2
0.4%
2159 3
0.6%
2157 1
 
0.2%

Campanha
Text

Missing 

Distinct78
Distinct (%)18.1%
Missing42
Missing (%)8.9%
Memory size3.8 KiB
2025-03-19T10:03:13.620856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length51
Median length16
Mean length19.983759
Min length13

Characters and Unicode

Total characters8613
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)9.3%

Sample

1st rowCampanha Brokers
2nd rowCampanha Starter
3rd rowCampanha Starter
4th rowCampanha Starter - São Paulo
5th rowCampanha Starter
ValueCountFrequency (%)
campanha 206
14.4%
181
12.6%
starter 130
 
9.1%
de 102
 
7.1%
leads 100
 
7.0%
geração 100
 
7.0%
produto 78
 
5.4%
brokers 62
 
4.3%
turbo 27
 
1.9%
grupo 25
 
1.7%
Other values (150) 422
29.4%
2025-03-19T10:03:14.022964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1292
15.0%
1008
 
11.7%
r 776
 
9.0%
e 637
 
7.4%
o 571
 
6.6%
t 397
 
4.6%
d 319
 
3.7%
n 298
 
3.5%
C 256
 
3.0%
s 251
 
2.9%
Other values (55) 2808
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1292
15.0%
1008
 
11.7%
r 776
 
9.0%
e 637
 
7.4%
o 571
 
6.6%
t 397
 
4.6%
d 319
 
3.7%
n 298
 
3.5%
C 256
 
3.0%
s 251
 
2.9%
Other values (55) 2808
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1292
15.0%
1008
 
11.7%
r 776
 
9.0%
e 637
 
7.4%
o 571
 
6.6%
t 397
 
4.6%
d 319
 
3.7%
n 298
 
3.5%
C 256
 
3.0%
s 251
 
2.9%
Other values (55) 2808
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1292
15.0%
1008
 
11.7%
r 776
 
9.0%
e 637
 
7.4%
o 571
 
6.6%
t 397
 
4.6%
d 319
 
3.7%
n 298
 
3.5%
C 256
 
3.0%
s 251
 
2.9%
Other values (55) 2808
32.6%

Tipo
Categorical

High correlation 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
Grupo
368 
Produto
78 
Turbo Leads
 
27

Length

Max length11
Median length5
Mean length5.6723044
Min length5

Characters and Unicode

Total characters2683
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrupo
2nd rowGrupo
3rd rowGrupo
4th rowGrupo
5th rowGrupo

Common Values

ValueCountFrequency (%)
Grupo 368
77.8%
Produto 78
 
16.5%
Turbo Leads 27
 
5.7%

Length

2025-03-19T10:03:14.153134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:14.240640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
grupo 368
73.6%
produto 78
 
15.6%
turbo 27
 
5.4%
leads 27
 
5.4%

Most occurring characters

ValueCountFrequency (%)
o 551
20.5%
u 473
17.6%
r 473
17.6%
G 368
13.7%
p 368
13.7%
d 105
 
3.9%
P 78
 
2.9%
t 78
 
2.9%
T 27
 
1.0%
b 27
 
1.0%
Other values (5) 135
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 551
20.5%
u 473
17.6%
r 473
17.6%
G 368
13.7%
p 368
13.7%
d 105
 
3.9%
P 78
 
2.9%
t 78
 
2.9%
T 27
 
1.0%
b 27
 
1.0%
Other values (5) 135
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 551
20.5%
u 473
17.6%
r 473
17.6%
G 368
13.7%
p 368
13.7%
d 105
 
3.9%
P 78
 
2.9%
t 78
 
2.9%
T 27
 
1.0%
b 27
 
1.0%
Other values (5) 135
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 551
20.5%
u 473
17.6%
r 473
17.6%
G 368
13.7%
p 368
13.7%
d 105
 
3.9%
P 78
 
2.9%
t 78
 
2.9%
T 27
 
1.0%
b 27
 
1.0%
Other values (5) 135
 
5.0%

Mensagem
Text

Missing 

Distinct376
Distinct (%)94.0%
Missing73
Missing (%)15.4%
Memory size3.8 KiB
2025-03-19T10:03:14.452562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length300
Median length266
Mean length242.7425
Min length94

Characters and Unicode

Total characters97097
Distinct characters94
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique355 ?
Unique (%)88.8%

Sample

1st rowOlá! Meu nome é Juliana Barbour, email juliana@skillsonline.com.br e telefone (11) 95081-0347. Eu vi o Reserva das Árvores - Paineiras no Apto e me interessei: https://apto.vc/br/sp/cotia/novo-lageado/reserva-das-arvores-paineiras Gostaria de tirar uma dúvida com você. :)
2nd rowOlá! Meu nome é Márcia, email marcinha_mg27@hotmail.com e telefone (21) 98868-9981. Eu vi o Residencial Pedra de Guaratiba no Apto e me interessei: https://apto.vc/br/rj/rio-de-janeiro/guaratiba/residencial-pedra-de-guaratiba Gostaria de tirar uma dúvida com você. :)
3rd rowOlá! Meu nome é Aline Rodrigues, email ali.rodrigues@outlook.com.br e telefone (11) 97655-4856. Eu vi o Nostro Mooca no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/mooca/nostro-mooca Gostaria de tirar uma dúvida com você. :)
4th rowOlá! Meu nome é Paloma Herbst, email pallomaherbst@gmail.com e telefone (11) 99695-7095. Eu vi o Reserva Direcional Limão no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/limao/reserva-direcional-limao Gostaria de tirar uma dúvida com você. :)
5th rowOlá! Meu nome é Luiz Fernando, email luiz.santos2981@gmail.com e telefone (41) 99721-4095. Eu vi o Residencial Turim no Apto e me interessei: https://apto.vc/br/pr/curitiba/portao/residencial-turim Gostaria de tirar uma dúvida com você. :)
ValueCountFrequency (%)
e 788
 
6.2%
de 429
 
3.4%
no 424
 
3.4%
396
 
3.1%
apto 392
 
3.1%
meu 389
 
3.1%
o 389
 
3.1%
eu 388
 
3.1%
vi 388
 
3.1%
olá 388
 
3.1%
Other values (1578) 8270
65.4%
2025-03-19T10:03:14.878238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12241
 
12.6%
a 7805
 
8.0%
e 7407
 
7.6%
o 6798
 
7.0%
i 5473
 
5.6%
t 4148
 
4.3%
r 3900
 
4.0%
s 3546
 
3.7%
n 3339
 
3.4%
m 3327
 
3.4%
Other values (84) 39113
40.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12241
 
12.6%
a 7805
 
8.0%
e 7407
 
7.6%
o 6798
 
7.0%
i 5473
 
5.6%
t 4148
 
4.3%
r 3900
 
4.0%
s 3546
 
3.7%
n 3339
 
3.4%
m 3327
 
3.4%
Other values (84) 39113
40.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12241
 
12.6%
a 7805
 
8.0%
e 7407
 
7.6%
o 6798
 
7.0%
i 5473
 
5.6%
t 4148
 
4.3%
r 3900
 
4.0%
s 3546
 
3.7%
n 3339
 
3.4%
m 3327
 
3.4%
Other values (84) 39113
40.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12241
 
12.6%
a 7805
 
8.0%
e 7407
 
7.6%
o 6798
 
7.0%
i 5473
 
5.6%
t 4148
 
4.3%
r 3900
 
4.0%
s 3546
 
3.7%
n 3339
 
3.4%
m 3327
 
3.4%
Other values (84) 39113
40.3%

Canal
Categorical

High correlation 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
WhatsApp
248 
Mensagem
140 
Chat
73 
Agendamento
 
12

Length

Max length11
Median length8
Mean length7.4587738
Min length4

Characters and Unicode

Total characters3528
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWhatsApp
2nd rowWhatsApp
3rd rowWhatsApp
4th rowWhatsApp
5th rowWhatsApp

Common Values

ValueCountFrequency (%)
WhatsApp 248
52.4%
Mensagem 140
29.6%
Chat 73
 
15.4%
Agendamento 12
 
2.5%

Length

2025-03-19T10:03:15.000472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:15.112160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
whatsapp 248
52.4%
mensagem 140
29.6%
chat 73
 
15.4%
agendamento 12
 
2.5%

Most occurring characters

ValueCountFrequency (%)
p 496
14.1%
a 473
13.4%
s 388
11.0%
t 333
9.4%
h 321
9.1%
e 304
8.6%
A 260
7.4%
W 248
7.0%
n 164
 
4.6%
g 152
 
4.3%
Other values (5) 389
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 496
14.1%
a 473
13.4%
s 388
11.0%
t 333
9.4%
h 321
9.1%
e 304
8.6%
A 260
7.4%
W 248
7.0%
n 164
 
4.6%
g 152
 
4.3%
Other values (5) 389
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 496
14.1%
a 473
13.4%
s 388
11.0%
t 333
9.4%
h 321
9.1%
e 304
8.6%
A 260
7.4%
W 248
7.0%
n 164
 
4.6%
g 152
 
4.3%
Other values (5) 389
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 496
14.1%
a 473
13.4%
s 388
11.0%
t 333
9.4%
h 321
9.1%
e 304
8.6%
A 260
7.4%
W 248
7.0%
n 164
 
4.6%
g 152
 
4.3%
Other values (5) 389
11.0%

Recibo
Text

Missing 

Distinct164
Distinct (%)97.0%
Missing304
Missing (%)64.3%
Memory size3.8 KiB
2025-03-19T10:03:15.408079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length255
Median length68
Mean length34.331361
Min length13

Characters and Unicode

Total characters5802
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)94.7%

Sample

1st rowHypnobox V.3: 871907
2nd rowSalesForce Direcional: 2025-03-18 08:06:00
3rd rowSalesForce Direcional: 2025-03-18 08:20:33
4th rowTenda Conecta: bc9c211a-01e5-41e3-b8aa-9bac20e7fb1b
5th rowHubspot Lucio: Obrigado por enviar o formulário. 2025-03-18 08:55:35
ValueCountFrequency (%)
hypnobox 41
 
6.3%
2025-03-18 36
 
5.5%
v.3 35
 
5.4%
salesforce 27
 
4.2%
direcional 24
 
3.7%
leads 23
 
3.5%
cyrela 18
 
2.8%
id 14
 
2.2%
studio 13
 
2.0%
cv 13
 
2.0%
Other values (247) 405
62.4%
2025-03-19T10:03:15.810145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
480
 
8.3%
o 301
 
5.2%
a 273
 
4.7%
e 271
 
4.7%
: 262
 
4.5%
2 245
 
4.2%
0 241
 
4.2%
3 209
 
3.6%
1 207
 
3.6%
5 185
 
3.2%
Other values (67) 3128
53.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5802
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
480
 
8.3%
o 301
 
5.2%
a 273
 
4.7%
e 271
 
4.7%
: 262
 
4.5%
2 245
 
4.2%
0 241
 
4.2%
3 209
 
3.6%
1 207
 
3.6%
5 185
 
3.2%
Other values (67) 3128
53.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5802
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
480
 
8.3%
o 301
 
5.2%
a 273
 
4.7%
e 271
 
4.7%
: 262
 
4.5%
2 245
 
4.2%
0 241
 
4.2%
3 209
 
3.6%
1 207
 
3.6%
5 185
 
3.2%
Other values (67) 3128
53.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5802
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
480
 
8.3%
o 301
 
5.2%
a 273
 
4.7%
e 271
 
4.7%
: 262
 
4.5%
2 245
 
4.2%
0 241
 
4.2%
3 209
 
3.6%
1 207
 
3.6%
5 185
 
3.2%
Other values (67) 3128
53.9%

Bonificado
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
Não
408 
Sim
65 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1419
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão
2nd rowNão
3rd rowNão
4th rowNão
5th rowNão

Common Values

ValueCountFrequency (%)
Não 408
86.3%
Sim 65
 
13.7%

Length

2025-03-19T10:03:15.920126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:15.990778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
não 408
86.3%
sim 65
 
13.7%

Most occurring characters

ValueCountFrequency (%)
N 408
28.8%
ã 408
28.8%
o 408
28.8%
S 65
 
4.6%
i 65
 
4.6%
m 65
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 408
28.8%
ã 408
28.8%
o 408
28.8%
S 65
 
4.6%
i 65
 
4.6%
m 65
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 408
28.8%
ã 408
28.8%
o 408
28.8%
S 65
 
4.6%
i 65
 
4.6%
m 65
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 408
28.8%
ã 408
28.8%
o 408
28.8%
S 65
 
4.6%
i 65
 
4.6%
m 65
 
4.6%

Motivo da Bonificação
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)6.2%
Missing408
Missing (%)86.3%
Memory size3.8 KiB
Bonificação automática por fidelidade
56 
Teste
Possível erro de integração
 
2
Aluguel / Busca Locação
 
1

Length

Max length37
Median length37
Mean length33.523077
Min length5

Characters and Unicode

Total characters2179
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st rowBonificação automática por fidelidade
2nd rowBonificação automática por fidelidade
3rd rowBonificação automática por fidelidade
4th rowBonificação automática por fidelidade
5th rowBonificação automática por fidelidade

Common Values

ValueCountFrequency (%)
Bonificação automática por fidelidade 56
 
11.8%
Teste 6
 
1.3%
Possível erro de integração 2
 
0.4%
Aluguel / Busca Locação 1
 
0.2%
(Missing) 408
86.3%

Length

2025-03-19T10:03:16.144052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:16.403309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bonificação 56
23.1%
automática 56
23.1%
por 56
23.1%
fidelidade 56
23.1%
teste 6
 
2.5%
possível 2
 
0.8%
erro 2
 
0.8%
de 2
 
0.8%
integração 2
 
0.8%
aluguel 1
 
0.4%
Other values (3) 3
 
1.2%

Most occurring characters

ValueCountFrequency (%)
i 282
12.9%
o 232
10.6%
a 228
10.5%
177
 
8.1%
d 170
 
7.8%
e 133
 
6.1%
t 120
 
5.5%
c 114
 
5.2%
f 112
 
5.1%
r 62
 
2.8%
Other values (18) 549
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2179
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 282
12.9%
o 232
10.6%
a 228
10.5%
177
 
8.1%
d 170
 
7.8%
e 133
 
6.1%
t 120
 
5.5%
c 114
 
5.2%
f 112
 
5.1%
r 62
 
2.8%
Other values (18) 549
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2179
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 282
12.9%
o 232
10.6%
a 228
10.5%
177
 
8.1%
d 170
 
7.8%
e 133
 
6.1%
t 120
 
5.5%
c 114
 
5.2%
f 112
 
5.1%
r 62
 
2.8%
Other values (18) 549
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2179
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 282
12.9%
o 232
10.6%
a 228
10.5%
177
 
8.1%
d 170
 
7.8%
e 133
 
6.1%
t 120
 
5.5%
c 114
 
5.2%
f 112
 
5.1%
r 62
 
2.8%
Other values (18) 549
25.2%

Elogio
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
Não
473 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1419
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão
2nd rowNão
3rd rowNão
4th rowNão
5th rowNão

Common Values

ValueCountFrequency (%)
Não 473
100.0%

Length

2025-03-19T10:03:16.676924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:16.749445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
não 473
100.0%

Most occurring characters

ValueCountFrequency (%)
N 473
33.3%
ã 473
33.3%
o 473
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 473
33.3%
ã 473
33.3%
o 473
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 473
33.3%
ã 473
33.3%
o 473
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 473
33.3%
ã 473
33.3%
o 473
33.3%

Crítica
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
Não
473 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1419
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão
2nd rowNão
3rd rowNão
4th rowNão
5th rowNão

Common Values

ValueCountFrequency (%)
Não 473
100.0%

Length

2025-03-19T10:03:16.824024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:16.877541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
não 473
100.0%

Most occurring characters

ValueCountFrequency (%)
N 473
33.3%
ã 473
33.3%
o 473
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 473
33.3%
ã 473
33.3%
o 473
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 473
33.3%
ã 473
33.3%
o 473
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 473
33.3%
ã 473
33.3%
o 473
33.3%

Repescagem
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
Não
473 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1419
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão
2nd rowNão
3rd rowNão
4th rowNão
5th rowNão

Common Values

ValueCountFrequency (%)
Não 473
100.0%

Length

2025-03-19T10:03:16.947021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:17.005675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
não 473
100.0%

Most occurring characters

ValueCountFrequency (%)
N 473
33.3%
ã 473
33.3%
o 473
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 473
33.3%
ã 473
33.3%
o 473
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 473
33.3%
ã 473
33.3%
o 473
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 473
33.3%
ã 473
33.3%
o 473
33.3%

Feedback
Unsupported

Missing  Rejected  Unsupported 

Missing473
Missing (%)100.0%
Memory size3.8 KiB

Criado
Real number (ℝ)

High correlation 

Distinct445
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45734.652
Minimum45734.325
Maximum45735.304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:17.561473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum45734.325
5-th percentile45734.382
Q145734.492
median45734.594
Q345734.824
95-th percentile45735.003
Maximum45735.304
Range0.97931713
Interquartile range (IQR)0.33208333

Descriptive statistics

Standard deviation0.21136959
Coefficient of variation (CV)4.6216509 × 10-6
Kurtosis-0.38271324
Mean45734.652
Median Absolute Deviation (MAD)0.1533912
Skewness0.60868314
Sum21632490
Variance0.044677106
MonotonicityIncreasing
2025-03-19T10:03:17.796353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45734.55721 3
 
0.6%
45734.55494 2
 
0.4%
45734.5527 2
 
0.4%
45734.38924 2
 
0.4%
45734.9944 2
 
0.4%
45734.553 2
 
0.4%
45734.55624 2
 
0.4%
45734.55532 2
 
0.4%
45734.55742 2
 
0.4%
45734.55051 2
 
0.4%
Other values (435) 452
95.6%
ValueCountFrequency (%)
45734.32454 1
0.2%
45734.32605 1
0.2%
45734.32771 1
0.2%
45734.33747 1
0.2%
45734.33791 1
0.2%
45734.34492 1
0.2%
45734.34674 1
0.2%
45734.34757 1
0.2%
45734.36199 1
0.2%
45734.36503 1
0.2%
ValueCountFrequency (%)
45735.30385 1
0.2%
45735.29725 1
0.2%
45735.28907 1
0.2%
45735.26029 1
0.2%
45735.22538 2
0.4%
45735.19372 1
0.2%
45735.17921 1
0.2%
45735.13497 1
0.2%
45735.07955 1
0.2%
45735.07934 1
0.2%

UTM Source
Categorical

High correlation 

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
chatbot
149 
direct
123 
facebook
70 
apto
67 
google
59 
Other values (3)
 
5

Length

Max length20
Median length14
Mean length6.3826638
Min length4

Characters and Unicode

Total characters3019
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowdirect
2nd rowgoogle
3rd rowdirect
4th rowgoogle
5th rowchatbot

Common Values

ValueCountFrequency (%)
chatbot 149
31.5%
direct 123
26.0%
facebook 70
14.8%
apto 67
14.2%
google 59
 
12.5%
Lifull-connect 2
 
0.4%
bing 2
 
0.4%
Facebook_Mobile_Feed 1
 
0.2%

Length

2025-03-19T10:03:17.949568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:18.065438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
chatbot 149
31.5%
direct 123
26.0%
facebook 70
14.8%
apto 67
14.2%
google 59
 
12.5%
lifull-connect 2
 
0.4%
bing 2
 
0.4%
facebook_mobile_feed 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
t 490
16.2%
o 479
15.9%
c 347
11.5%
a 287
9.5%
e 258
8.5%
b 223
7.4%
h 149
 
4.9%
i 128
 
4.2%
d 124
 
4.1%
r 123
 
4.1%
Other values (12) 411
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 490
16.2%
o 479
15.9%
c 347
11.5%
a 287
9.5%
e 258
8.5%
b 223
7.4%
h 149
 
4.9%
i 128
 
4.2%
d 124
 
4.1%
r 123
 
4.1%
Other values (12) 411
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 490
16.2%
o 479
15.9%
c 347
11.5%
a 287
9.5%
e 258
8.5%
b 223
7.4%
h 149
 
4.9%
i 128
 
4.2%
d 124
 
4.1%
r 123
 
4.1%
Other values (12) 411
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 490
16.2%
o 479
15.9%
c 347
11.5%
a 287
9.5%
e 258
8.5%
b 223
7.4%
h 149
 
4.9%
i 128
 
4.2%
d 124
 
4.1%
r 123
 
4.1%
Other values (12) 411
13.6%

UTM Medium
Categorical

High correlation 

Distinct9
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
wpp
149 
none
123 
email
67 
wpp_leadads
59 
cpc
52 
Other values (4)
23 

Length

Max length11
Median length7
Mean length4.7188161
Min length3

Characters and Unicode

Total characters2232
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownone
2nd rowcpc
3rd rownone
4th rowcpc
5th rowwpp

Common Values

ValueCountFrequency (%)
wpp 149
31.5%
none 123
26.0%
email 67
14.2%
wpp_leadads 59
 
12.5%
cpc 52
 
11.0%
leadads 9
 
1.9%
organic 9
 
1.9%
display 3
 
0.6%
CPC 2
 
0.4%

Length

2025-03-19T10:03:18.300121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:18.423007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
wpp 149
31.5%
none 123
26.0%
email 67
14.2%
wpp_leadads 59
 
12.5%
cpc 54
 
11.4%
leadads 9
 
1.9%
organic 9
 
1.9%
display 3
 
0.6%

Most occurring characters

ValueCountFrequency (%)
p 471
21.1%
e 258
11.6%
n 255
11.4%
a 215
9.6%
w 208
9.3%
d 139
 
6.2%
l 138
 
6.2%
o 132
 
5.9%
c 113
 
5.1%
i 79
 
3.5%
Other values (8) 224
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 471
21.1%
e 258
11.6%
n 255
11.4%
a 215
9.6%
w 208
9.3%
d 139
 
6.2%
l 138
 
6.2%
o 132
 
5.9%
c 113
 
5.1%
i 79
 
3.5%
Other values (8) 224
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 471
21.1%
e 258
11.6%
n 255
11.4%
a 215
9.6%
w 208
9.3%
d 139
 
6.2%
l 138
 
6.2%
o 132
 
5.9%
c 113
 
5.1%
i 79
 
3.5%
Other values (8) 224
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 471
21.1%
e 258
11.6%
n 255
11.4%
a 215
9.6%
w 208
9.3%
d 139
 
6.2%
l 138
 
6.2%
o 132
 
5.9%
c 113
 
5.1%
i 79
 
3.5%
Other values (8) 224
10.0%

UTM Campaign
Categorical

High correlation  Missing 

Distinct15
Distinct (%)5.4%
Missing193
Missing (%)40.8%
Memory size3.8 KiB
pgproduto
71 
jornadab2c
66 
leadads_produto_t1
33 
bairros
28 
recomendacao_ativa
23 
Other values (10)
59 

Length

Max length19
Median length15
Mean length10.657143
Min length4

Characters and Unicode

Total characters2984
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowpg_busca
2nd rowleadads_produto_t1
3rd rowhome
4th rowpgproduto
5th rowleadads_produto_t1

Common Values

ValueCountFrequency (%)
pgproduto 71
 
15.0%
jornadab2c 66
 
14.0%
leadads_produto_t1 33
 
7.0%
bairros 28
 
5.9%
recomendacao_ativa 23
 
4.9%
ctwa 15
 
3.2%
home 15
 
3.2%
leadads_bairros 12
 
2.5%
pg_busca 3
 
0.6%
performance_max 3
 
0.6%
Other values (5) 11
 
2.3%
(Missing) 193
40.8%

Length

2025-03-19T10:03:18.581196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pgproduto 71
25.4%
jornadab2c 66
23.6%
leadads_produto_t1 33
11.8%
bairros 28
 
10.0%
recomendacao_ativa 23
 
8.2%
ctwa 15
 
5.4%
home 15
 
5.4%
leadads_bairros 12
 
4.3%
pg_busca 3
 
1.1%
performance_max 3
 
1.1%
Other values (5) 11
 
3.9%

Most occurring characters

ValueCountFrequency (%)
a 397
13.3%
o 388
13.0%
d 292
 
9.8%
r 286
 
9.6%
p 188
 
6.3%
t 183
 
6.1%
c 140
 
4.7%
e 116
 
3.9%
u 112
 
3.8%
b 110
 
3.7%
Other values (17) 772
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 397
13.3%
o 388
13.0%
d 292
 
9.8%
r 286
 
9.6%
p 188
 
6.3%
t 183
 
6.1%
c 140
 
4.7%
e 116
 
3.9%
u 112
 
3.8%
b 110
 
3.7%
Other values (17) 772
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 397
13.3%
o 388
13.0%
d 292
 
9.8%
r 286
 
9.6%
p 188
 
6.3%
t 183
 
6.1%
c 140
 
4.7%
e 116
 
3.9%
u 112
 
3.8%
b 110
 
3.7%
Other values (17) 772
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 397
13.3%
o 388
13.0%
d 292
 
9.8%
r 286
 
9.6%
p 188
 
6.3%
t 183
 
6.1%
c 140
 
4.7%
e 116
 
3.9%
u 112
 
3.8%
b 110
 
3.7%
Other values (17) 772
25.9%

UTM Content
Categorical

High correlation  Missing 

Distinct29
Distinct (%)11.5%
Missing220
Missing (%)46.5%
Memory size3.8 KiB
chat
86 
d0
62 
mensagem
52 
sao_paulo
10 
guarulhos
 
5
Other values (24)
38 

Length

Max length19
Median length18
Mean length5.6561265
Min length2

Characters and Unicode

Total characters1431
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)5.1%

Sample

1st rowrio_de_janeiro
2nd rowsao_paulo
3rd rowchat
4th rowneo-brooklin
5th rowmensagem

Common Values

ValueCountFrequency (%)
chat 86
 
18.2%
d0 62
 
13.1%
mensagem 52
 
11.0%
sao_paulo 10
 
2.1%
guarulhos 5
 
1.1%
1422913285558099 3
 
0.6%
2066476867034528 3
 
0.6%
d1 3
 
0.6%
starter_plus 2
 
0.4%
paginas_busca 2
 
0.4%
Other values (19) 25
 
5.3%
(Missing) 220
46.5%

Length

2025-03-19T10:03:18.719263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chat 86
34.0%
d0 62
24.5%
mensagem 52
20.6%
sao_paulo 10
 
4.0%
guarulhos 5
 
2.0%
1422913285558099 3
 
1.2%
2066476867034528 3
 
1.2%
d1 3
 
1.2%
starter_plus 2
 
0.8%
paginas_busca 2
 
0.8%
Other values (19) 25
 
9.9%

Most occurring characters

ValueCountFrequency (%)
a 194
13.6%
e 127
 
8.9%
m 108
 
7.5%
t 107
 
7.5%
c 97
 
6.8%
h 92
 
6.4%
s 81
 
5.7%
0 75
 
5.2%
d 74
 
5.2%
n 65
 
4.5%
Other values (30) 411
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1431
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 194
13.6%
e 127
 
8.9%
m 108
 
7.5%
t 107
 
7.5%
c 97
 
6.8%
h 92
 
6.4%
s 81
 
5.7%
0 75
 
5.2%
d 74
 
5.2%
n 65
 
4.5%
Other values (30) 411
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1431
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 194
13.6%
e 127
 
8.9%
m 108
 
7.5%
t 107
 
7.5%
c 97
 
6.8%
h 92
 
6.4%
s 81
 
5.7%
0 75
 
5.2%
d 74
 
5.2%
n 65
 
4.5%
Other values (30) 411
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1431
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 194
13.6%
e 127
 
8.9%
m 108
 
7.5%
t 107
 
7.5%
c 97
 
6.8%
h 92
 
6.4%
s 81
 
5.7%
0 75
 
5.2%
d 74
 
5.2%
n 65
 
4.5%
Other values (30) 411
28.7%

IP
Text

Distinct246
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:19.140394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.082452
Min length10

Characters and Unicode

Total characters6188
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique175 ?
Unique (%)37.0%

Sample

1st row80.193.51.9
2nd row177.121.98.139
3rd row191.39.151.2
4th row177.98.108.75
5th row200.193.191.156
ValueCountFrequency (%)
34.90.109.174 37
 
7.8%
34.91.221.79 27
 
5.7%
34.90.201.190 23
 
4.9%
34.90.47.65 19
 
4.0%
163.116.233.48 9
 
1.9%
187.43.131.255 8
 
1.7%
189.28.48.78 8
 
1.7%
189.69.81.97 7
 
1.5%
179.34.50.178 6
 
1.3%
20.191.65.166 5
 
1.1%
Other values (236) 324
68.5%
2025-03-19T10:03:19.556994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1419
22.9%
1 1074
17.4%
2 608
9.8%
9 509
 
8.2%
7 475
 
7.7%
4 462
 
7.5%
0 402
 
6.5%
3 401
 
6.5%
8 314
 
5.1%
5 293
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1419
22.9%
1 1074
17.4%
2 608
9.8%
9 509
 
8.2%
7 475
 
7.7%
4 462
 
7.5%
0 402
 
6.5%
3 401
 
6.5%
8 314
 
5.1%
5 293
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1419
22.9%
1 1074
17.4%
2 608
9.8%
9 509
 
8.2%
7 475
 
7.7%
4 462
 
7.5%
0 402
 
6.5%
3 401
 
6.5%
8 314
 
5.1%
5 293
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1419
22.9%
1 1074
17.4%
2 608
9.8%
9 509
 
8.2%
7 475
 
7.7%
4 462
 
7.5%
0 402
 
6.5%
3 401
 
6.5%
8 314
 
5.1%
5 293
 
4.7%

Últ. Atualização
Real number (ℝ)

High correlation 

Distinct454
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45734.653
Minimum45734.325
Maximum45735.304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-19T10:03:19.677733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum45734.325
5-th percentile45734.382
Q145734.492
median45734.594
Q345734.824
95-th percentile45735.003
Maximum45735.304
Range0.97934028
Interquartile range (IQR)0.3312037

Descriptive statistics

Standard deviation0.21091542
Coefficient of variation (CV)4.6117203 × 10-6
Kurtosis-0.37381822
Mean45734.653
Median Absolute Deviation (MAD)0.15314815
Skewness0.60746512
Sum21632491
Variance0.044485316
MonotonicityNot monotonic
2025-03-19T10:03:19.822174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45734.55762 4
 
0.8%
45734.55723 2
 
0.4%
45734.55668 2
 
0.4%
45734.99442 2
 
0.4%
45734.48097 2
 
0.4%
45734.553 2
 
0.4%
45734.5545 2
 
0.4%
45734.55451 2
 
0.4%
45734.55494 2
 
0.4%
45734.55624 2
 
0.4%
Other values (444) 451
95.3%
ValueCountFrequency (%)
45734.32454 1
0.2%
45734.32605 1
0.2%
45734.32773 1
0.2%
45734.3375 1
0.2%
45734.33791 1
0.2%
45734.34492 1
0.2%
45734.34674 1
0.2%
45734.3476 1
0.2%
45734.36199 1
0.2%
45734.36503 1
0.2%
ValueCountFrequency (%)
45735.30388 1
0.2%
45735.29727 1
0.2%
45735.28911 1
0.2%
45735.26029 1
0.2%
45735.22545 1
0.2%
45735.22543 1
0.2%
45735.19372 1
0.2%
45735.17926 1
0.2%
45735.135 1
0.2%
45735.07955 1
0.2%

Unnamed: 34
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
45734
449 
45735
 
24

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2365
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45734
2nd row45734
3rd row45734
4th row45734
5th row45734

Common Values

ValueCountFrequency (%)
45734 449
94.9%
45735 24
 
5.1%

Length

2025-03-19T10:03:19.967648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T10:03:20.040811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
45734 449
94.9%
45735 24
 
5.1%

Most occurring characters

ValueCountFrequency (%)
4 922
39.0%
5 497
21.0%
7 473
20.0%
3 473
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2365
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 922
39.0%
5 497
21.0%
7 473
20.0%
3 473
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2365
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 922
39.0%
5 497
21.0%
7 473
20.0%
3 473
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2365
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 922
39.0%
5 497
21.0%
7 473
20.0%
3 473
20.0%

Interactions

2025-03-19T10:03:04.400470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-19T10:02:58.957486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:02:59.684443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:00.441745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:01.184092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:01.999345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:02.910379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:03.716910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:04.479465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:02:58.294973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:02:59.034789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:02:59.836770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:00.518395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:01.277684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:02.076485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:02.999926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:03.788662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:04.563042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:02:58.372471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:02:59.116661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:02:59.913362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:00.598984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:01.383805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:02.154419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:03.084127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:03.866567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:04.631310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-19T10:02:59.198287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:02:59.982499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:00.672517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:01.468954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:02.234173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:03.153074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:03.945171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:04.711435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:02:58.523703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:02:59.279070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:00.062526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:00.748130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:01.554074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:02.355724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:03.225289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:04.018240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:04.799164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:02:58.616440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-19T10:02:58.874694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:02:59.604263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:00.371437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:01.100098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:01.895663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:02.814961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:03.639406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T10:03:04.325889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-19T10:03:20.143771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BonificadoCanalCidadeContratanteCriadoDDDEstadoIDID AnuncianteID CampanhaID ClienteID ContratanteID ImóvelMotivo da BonificaçãoPlanoTelefoneTipoUTM CampaignUTM ContentUTM MediumUTM SourceUnnamed: 34Últ. Atualização
Bonificado1.0000.0000.0000.0000.0730.0000.0000.1630.0000.1730.1150.0000.1221.0000.0000.0680.0000.1580.0000.1450.1090.0000.074
Canal0.0001.0000.1480.0000.1410.1340.0820.1820.3490.2450.0760.0000.1150.2600.5880.1360.4040.5770.7210.4400.4360.0650.142
Cidade0.0000.1481.0001.0000.2200.7180.9750.1610.3170.2350.0001.0000.3020.0000.3600.0470.3010.4110.4580.1590.2980.3030.219
Contratante0.0000.0001.0001.0000.0000.0001.0000.3090.9260.7400.0001.0000.408NaN1.0000.0000.0000.0000.0000.0000.0001.0000.000
Criado0.0730.1410.2200.0001.000-0.0250.0831.000-0.056-0.1110.4360.000-0.0220.0000.0510.0210.1030.2840.3390.2520.2460.9690.998
DDD0.0000.1340.7180.000-0.0251.0000.747-0.0250.013-0.1170.1460.0000.1660.2940.1370.4920.3030.3530.5550.2230.1760.108-0.019
Estado0.0000.0820.9751.0000.0830.7471.0000.0860.2530.1730.0001.0000.0750.0000.1660.0270.2450.2670.4300.0990.0680.1320.078
ID0.1630.1820.1610.3091.000-0.0250.0861.000-0.055-0.1110.4360.309-0.0220.0000.0550.0210.1230.3350.3210.2620.2840.6750.998
ID Anunciante0.0000.3490.3170.926-0.0560.0130.253-0.0551.0000.442-0.0900.926-0.0030.0710.810-0.1210.3630.2950.1200.1860.1830.158-0.054
ID Campanha0.1730.2450.2350.740-0.111-0.1170.173-0.1110.4421.000-0.0700.7400.1580.1990.342-0.0730.1040.1270.0000.0560.0950.103-0.112
ID Cliente0.1150.0760.0000.0000.4360.1460.0000.436-0.090-0.0701.0000.0000.0710.6120.0000.0780.1260.5050.2990.0600.0480.0000.433
ID Contratante0.0000.0001.0001.0000.0000.0001.0000.3090.9260.7400.0001.0000.408NaN1.0000.0000.0000.0000.0000.0000.0001.0000.000
ID Imóvel0.1220.1150.3020.408-0.0220.1660.075-0.022-0.0030.1580.0710.4081.0000.0000.2120.0750.2030.1980.3420.1240.1430.000-0.027
Motivo da Bonificação1.0000.2600.000NaN0.0000.2940.0000.0000.0710.1990.612NaN0.0001.0000.0240.2080.2330.0000.3070.2610.3180.0000.000
Plano0.0000.5880.3601.0000.0510.1370.1660.0550.8100.3420.0001.0000.2120.0241.0000.0450.4360.4500.2830.3880.3880.0000.061
Telefone0.0680.1360.0470.0000.0210.4920.0270.021-0.121-0.0730.0780.0000.0750.2080.0451.0000.1850.4500.3710.2750.2960.0000.025
Tipo0.0000.4040.3010.0000.1030.3030.2450.1230.3630.1040.1260.0000.2030.2330.4360.1851.0000.5880.5870.6120.4850.0200.084
UTM Campaign0.1580.5770.4110.0000.2840.3530.2670.3350.2950.1270.5050.0000.1980.0000.4500.4500.5881.0000.6910.9650.9650.0000.284
UTM Content0.0000.7210.4580.0000.3390.5550.4300.3210.1200.0000.2990.0000.3420.3070.2830.3710.5870.6911.0000.9540.9540.3120.342
UTM Medium0.1450.4400.1590.0000.2520.2230.0990.2620.1860.0560.0600.0000.1240.2610.3880.2750.6120.9650.9541.0000.8690.1340.249
UTM Source0.1090.4360.2980.0000.2460.1760.0680.2840.1830.0950.0480.0000.1430.3180.3880.2960.4850.9650.9540.8691.0000.0420.245
Unnamed: 340.0000.0650.3031.0000.9690.1080.1320.6750.1580.1030.0001.0000.0000.0000.0000.0000.0200.0000.3120.1340.0421.0000.969
Últ. Atualização0.0740.1420.2190.0000.998-0.0190.0780.998-0.054-0.1120.4330.000-0.0270.0000.0610.0250.0840.2840.3420.2490.2450.9691.000

Missing values

2025-03-19T10:03:05.277229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-19T10:03:05.509901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-19T10:03:06.165879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDID ClienteClienteE-mailDDDTelefoneID ImóvelImóvelPlanoCidadeEstadoID AnuncianteAnuncianteID ContratanteContratanteID CampanhaCampanhaTipoMensagemCanalReciboBonificadoMotivo da BonificaçãoElogioCríticaRepescagemFeedbackCriadoUTM SourceUTM MediumUTM CampaignUTM ContentIPÚlt. AtualizaçãoUnnamed: 34
0828769477917Juliana Barbourjuliana@skillsonline.com.br119508103472072Reserva das Árvores - PaineirasBrokersCotiaSão Paulo484Imo House Negócios ImobiliáriosNaNNaN1703.0Campanha BrokersGrupoOlá! Meu nome é Juliana Barbour, email juliana@skillsonline.com.br e telefone (11) 95081-0347.\n \nEu vi o Reserva das Árvores - Paineiras no Apto e me interessei: https://apto.vc/br/sp/cotia/novo-lageado/reserva-das-arvores-paineiras\n \nGostaria de tirar uma dúvida com você. :)WhatsAppNaNNãoNaNNãoNãoNãoNaN45734.324537directnoneNaNNaN80.193.51.945734.32453745734
1828770477810Márciamarcinha_mg27@hotmail.com219886899815585Residencial Pedra de GuaratibaBusinessRio de JaneiroRio de Janeiro690MRVNaNNaN2033.0Campanha StarterGrupoOlá! Meu nome é Márcia, email marcinha_mg27@hotmail.com e telefone (21) 98868-9981.\n \nEu vi o Residencial Pedra de Guaratiba no Apto e me interessei: https://apto.vc/br/rj/rio-de-janeiro/guaratiba/residencial-pedra-de-guaratiba\n \nGostaria de tirar uma dúvida com você. :)WhatsAppNaNNãoNaNNãoNãoNãoNaN45734.326053googlecpcNaNrio_de_janeiro177.121.98.13945734.32605345734
2828771477918Aline Rodriguesali.rodrigues@outlook.com.br119765548566252Nostro MoocaBusinessSão PauloSão Paulo570EmccampNaNNaN1920.0Campanha StarterGrupoOlá! Meu nome é Aline Rodrigues, email ali.rodrigues@outlook.com.br e telefone (11) 97655-4856.\n \nEu vi o Nostro Mooca no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/mooca/nostro-mooca\n \nGostaria de tirar uma dúvida com você. :)WhatsAppHypnobox V.3: 871907NãoNaNNãoNãoNãoNaN45734.327708directnoneNaNNaN191.39.151.245734.32773145734
3828772477920Paloma Herbstpallomaherbst@gmail.com119969570954832Reserva Direcional LimãoBusinessSão PauloSão Paulo714Direcional EngenhariaNaNNaN1161.0Campanha Starter - São PauloGrupoOlá! Meu nome é Paloma Herbst, email pallomaherbst@gmail.com e telefone (11) 99695-7095.\n \nEu vi o Reserva Direcional Limão no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/limao/reserva-direcional-limao\n \nGostaria de tirar uma dúvida com você. :)WhatsAppSalesForce Direcional: 2025-03-18 08:06:00NãoNaNNãoNãoNãoNaN45734.337465googlecpcNaNsao_paulo177.98.108.7545734.33750045734
4828773477919Luiz Fernandoluiz.santos2981@gmail.com419972140957781Residencial TurimBusinessCuritibaParaná2382TNR ConstrutoraNaNNaN1820.0Campanha StarterGrupoOlá! Meu nome é Luiz Fernando, email luiz.santos2981@gmail.com e telefone (41) 99721-4095.\n \nEu vi o Residencial Turim no Apto e me interessei: https://apto.vc/br/pr/curitiba/portao/residencial-turim\n \nGostaria de tirar uma dúvida com você. :)WhatsAppNaNNãoNaNNãoNãoNãoNaN45734.337905chatbotwpppg_buscachat200.193.191.15645734.33790545734
5828774477922Livia Amorimamorimdeoliveira@gmail.com119892798116717Residencial Ritmos da BarraBrokersSão PauloSão Paulo2055Aline Teixeira da SilvaNaNNaN2001.0Geração de LeadsGrupoOlá! Meu nome é Livia Amorim, email amorimdeoliveira@gmail.com e telefone (11) 98927-9811.\n \nEu vi o Residencial Ritmos da Barra no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/barra-funda/residencial-ritmos-da-barra\n \nGostaria de tirar uma dúvida com você. :)WhatsAppNaNNãoNaNNãoNãoNãoNaN45734.344919directnoneNaNNaN200.169.33.4645734.34491945734
6828775477923Fátima Thoméfatithome@hotmail.com629818300713118Neo BrooklinBusinessSão PauloSão Paulo155Tegra Incorporadora5.0Tegra SP1334.0Grupo - Tegra SP - Zona SulGrupoNaNChatNaNNãoNaNNãoNãoNãoNaN45734.346736googlecpcNaNneo-brooklin177.174.219.3345734.34673645734
7828776477924Juliano Sallesjotasaamaral@icloud.com199890080006099Village NovusBusinessRibeirão PretoSão Paulo714Direcional EngenhariaNaNNaN1161.0Campanha Starter - São PauloGrupoOlá! Meu nome é Juliano Salles, email jotasaamaral@icloud.com e telefone (19) 98900-8000.\n \nEu vi o Village Novus no Apto e me interessei: https://apto.vc/br/sp/ribeirao-preto/jardim-guapore/village-novus\n \nGostaria de tirar uma dúvida com você. :)WhatsAppSalesForce Direcional: 2025-03-18 08:20:33NãoNaNNãoNãoNãoNaN45734.347569directnoneNaNNaN189.76.71.22545734.34760445734
8828777477929Camila Brassolatticaca_brassolatti@hotmail.com199836118116653Sensia TaquaralBusinessCampinasSão Paulo1472Sensia IncorporadoraNaNNaN2076.0Produto - Sensia TaquaralProdutoOlá! Meu nome é Camila Brassolatti, email caca_brassolatti@hotmail.com e telefone (19) 98361-1811. Eu vi o Sensia Taquaral no Apto e me interessei: https://apto.vc/br/sp/campinas/taquaral/sensia-taquaral. Gostaria de tirar uma dúvida com você. :)MensagemNaNNãoNaNNãoNãoNãoNaN45734.361991chatbotwppleadads_produto_t1mensagem34.90.201.19045734.36199145734
9828778255321Fernanda Azevedofercristina301@gmail.com119849050421438Square CarapicuíbaBrokersCarapicuíbaSão Paulo484Imo House Negócios ImobiliáriosNaNNaN1703.0Campanha BrokersGrupoOlá! Meu nome é Fernanda Azevedo, email fercristina301@gmail.com e telefone (11) 98490-5042.\n \nEu vi o Square Carapicuíba no Apto e me interessei: https://apto.vc/br/sp/carapicuiba/jardim-das-belezas/square-carapicuiba\n \nGostaria de tirar uma dúvida com você. :)WhatsAppNaNNãoNaNNãoNãoNãoNaN45734.365035chatbotwpphomechat187.43.130.9545734.36503545734
IDID ClienteClienteE-mailDDDTelefoneID ImóvelImóvelPlanoCidadeEstadoID AnuncianteAnuncianteID ContratanteContratanteID CampanhaCampanhaTipoMensagemCanalReciboBonificadoMotivo da BonificaçãoElogioCríticaRepescagemFeedbackCriadoUTM SourceUTM MediumUTM CampaignUTM ContentIPÚlt. AtualizaçãoUnnamed: 34
463829232478147Zoulfikarkmatizoulfikar@gmail.com119135612727053Aria MoocaBrokersSão PauloSão Paulo1794Claudevania Almeida MoraisNaNNaN1582.0Geração de LeadsGrupoOlá! Meu nome é Zoulfikar, email kmatizoulfikar@gmail.com e telefone (11) 91356-1272.\n \nEu vi o Aria Mooca no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/mooca/aria-mooca\n \nGostaria de tirar uma dúvida com você. :)WhatsAppNaNNãoNaNNãoNãoNãoNaN45735.079549chatbotwpppgprodutochat152.254.129.21345735.07954945735
464829233478148Roseli Mascarenhas Santosroseli.santos1606@gmail.com119830725268103Vitta Vila MarianaBusinessSão PauloSão Paulo1023SulplanNaNNaN2069.0Produto - Vitta Vila MarianaProdutoOlá! Meu nome é Roseli Mascarenhas Santos, email roseli.santos1606@gmail.com e telefone (11) 98307-2526. Eu vi o Vitta Vila Mariana no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/vila-mariana/vitta-vila-mariana. Gostaria de tirar uma dúvida com você. :)MensagemApto Connect: Integração externa completada com sucesso. (19/03/2025 03:14:24)NãoNaNNãoNãoNãoNaN45735.134965facebookwpp_leadadsbairrosNaN34.90.47.6545735.13500045735
465829234478123Glenda dos Anjosglendaanjos100@gmail.com129826929676871HarmoniaBusinessSão José dos CamposSão Paulo970Árbore EngenhariaNaNNaN1436.0Campanha StarterGrupoOlá! Meu nome é Glenda dos Anjos, email glendaanjos100@gmail.com e telefone (12) 98269-2967.\n \nEu vi o Harmonia no Apto e me interessei: https://apto.vc/br/sp/sao-jose-dos-campos/conjunto-residencial-galo-branco/harmonia\n \nGostaria de tirar uma dúvida com você. :)WhatsAppCV Leads: ID 170550SimBonificação automática por fidelidadeNãoNãoNãoNaN45735.179213directnoneNaNNaN177.62.127.1745735.17925945735
466829235478149Andressaandressalimmaa@gmail.com119745841986257Connect Vila SôniaBrokersSão PauloSão Paulo1991Anderson de Mello BarbosaNaNNaN1943.0Geração de LeadsGrupoOlá! Meu nome é Andressa, email andressalimmaa@gmail.com e telefone (11) 97458-4198.\n \nEu vi o Connect Vila Sônia no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/vila-sonia/connect-vila-sonia\n \nGostaria de tirar uma dúvida com você. :)WhatsAppNaNNãoNaNNãoNãoNãoNaN45735.193715directnoneNaNNaN45.6.35.3745735.19371545735
467829236478150Aldecy Dinizaldecydiniz34@gmail.com619936828297936Bosque Santa Cruz by Diálogo ResidencesBusinessSão PauloSão Paulo355DiálogoNaNNaN1916.0Turbo - Grupo Alto do IpirangaTurbo LeadsOlá! Meu nome é Aldecy Diniz, email aldecydiniz34@gmail.com e telefone (61) 99368-2829. Eu vi o Bosque Santa Cruz by Diálogo Residences no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/alto-do-ipiranga/bosque-santa-cruz-by-dialogo-residences. Gostaria de tirar uma dúvida com você. :)MensagemHypnobox V.3: 1233113NãoNaNNãoNãoNãoNaN45735.225382facebookleadadsNaN1356329168061671189.28.48.7845735.22545145735
468829237478150Aldecy Dinizaldecydiniz34@gmail.com619936828297936Bosque Santa Cruz by Diálogo ResidencesBusinessSão PauloSão Paulo355DiálogoNaNNaN1916.0Turbo - Grupo Alto do IpirangaTurbo LeadsOlá! Meu nome é Aldecy Diniz, email aldecydiniz34@gmail.com e telefone (61) 99368-2829. Eu vi o Bosque Santa Cruz by Diálogo Residences no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/alto-do-ipiranga/bosque-santa-cruz-by-dialogo-residences. Gostaria de tirar uma dúvida com você. :)MensagemHypnobox V.3: Mensagem duplicadaSimBonificação automática por fidelidadeNãoNãoNãoNaN45735.225382facebookleadadsNaN1356329168061671189.28.48.7845735.22542845735
469829238478152Laryssalaryssa.leal2304@gmail.com119825448857857Near GuarapirangaBrokersSão PauloSão Paulo1212Davi Carlos Chaves da MourariaNaNNaN1752.0Campanha BrokersGrupoOlá! Meu nome é Laryssa, email laryssa.leal2304@gmail.com e telefone (11) 98254-4885.\n \nEu vi o Near Guarapiranga no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/guarapiranga/near-guarapiranga\n \nGostaria de tirar uma dúvida com você. :)WhatsAppNaNNãoNaNNãoNãoNãoNaN45735.260289directnoneNaNNaN170.81.156.10045735.26028945735
470829239270598Camila Davansocamila@davanso.com.br119987535177398Bosque Diálogo Jd. PrudênciaBusinessSão PauloSão Paulo355DiálogoNaNNaN1378.0Grupo - OngoingGrupoOlá! Meu nome é Camila Davanso, email camila@davanso.com.br e telefone (11) 99875-3517. Eu vi o Bosque Diálogo Jd. Prudência no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/jardim-prudencia/bosque-dialogo-jd-prudencia. Gostaria de tirar uma dúvida com você. :)MensagemHypnobox V.3: 1233128NãoNaNNãoNãoNãoNaN45735.289074chatbotwpprecomendacao_ativamensagem34.90.47.6545735.28910945735
471829240478153Gerson Cosme Santosgersonskill@yahoo.com.br119995436597255KA’A Home BoutiqueBusinessBarueriSão Paulo508CNA SpitalettiNaNNaN1837.0Produto - KAA Home BoutiqueProdutoOlá! Meu nome é Gerson Cosme Santos, email gersonskill@yahoo.com.br e telefone (11) 99954-3659. Eu vi o KA’A Home Boutique no Apto e me interessei: https://apto.vc/br/sp/barueri/alphaville/kaa-home-boutique. Gostaria de tirar uma dúvida com você. :)MensagemApto Connect: Integração externa completada com sucesso. (19/03/2025 07:08:04)NãoNaNNãoNãoNãoNaN45735.297245chatbotwppleadads_produto_t1mensagem34.91.221.7945735.29726945735
472829241477645Madalena Tominagamadalena.tominaga@gmail.com119840344844803My Life VergueiroBusinessSão PauloSão Paulo2040MaskanNaNNaN1511.0Campanha StarterGrupoOlá! Meu nome é Madalena Tominaga, email madalena.tominaga@gmail.com e telefone (11) 98403-4484. Eu vi o My Life Vergueiro no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/vergueiro/my-life-vergueiro. Gostaria de tirar uma dúvida com você. :)MensagemSigavi: MPD: 30117NãoNaNNãoNãoNãoNaN45735.303854chatbotwpprecomendacao_ativamensagem34.91.221.7945735.30387745735